{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 深度学习模型训练和关键参数调优详解\n",
    "\n",
    "针对特定场景任务从**模型选择、模型训练、超参优化、效果展示**这四个方面进行模型开发。\n",
    "\n",
    "参考资料：\n",
    "- [最实用的项目开发全流程!从自建数据集到模型部署,其实没有这么难!](https://aistudio.baidu.com/aistudio/projectdetail/671871)\n",
    "- [神经网络前向传播原理详解](https://aistudio.baidu.com/aistudio/projectdetail/742777)\n",
    "- [写给小白的入门笔记，神经网络梯度下降原理详解](https://aistudio.baidu.com/aistudio/projectdetail/757995)\n",
    "- [使用飞桨可解释性算法库InterpretDL解析人脸识别模型](https://aistudio.baidu.com/aistudio/projectdetail/1658188)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 一、模型选择\n",
    "\n",
    "从任务类型出发，选择最合适的模型。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 1.回归任务\n",
    "\n",
    "从使用Numpy推导，到使用深度学习框架，一步步走进最简单的回归任务：[当人工智能邂逅蓝桥杯算法题，会擦出怎样的火花？](https://aistudio.baidu.com/aistudio/projectdetail/1693536)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### 人脸关键点检测\n",
    "\n",
    "完整代码详见[基于空间注意力SAM的GoogLeNet实现人脸关键点检测并自动添加表情贴纸](https://aistudio.baidu.com/aistudio/projectdetail/1533049)\n",
    "\n",
    "![](https://ai-studio-static-online.cdn.bcebos.com/0ccfc3abc6574d18aba1649687a464730f426b839b9c4a409eb2f75629fce9dc)\n",
    "\n",
    "\n",
    "68点的人脸关键点检测：\n",
    "\n",
    "<img style=\"display: block; margin: 0 auto;zoom:30%;\" src=\"https://ai-studio-static-online.cdn.bcebos.com/a9161a11b91b468497e9fbbf565a0a638ace47b2a6c54748a7bd2e497ea0727f\" alt=\"\"/>\n",
    "\n",
    "- 1-17：人脸的下轮廓\n",
    "- 18-27：眉毛\n",
    "- 28-36： 鼻子\n",
    "- 37-48：眼睛\n",
    "- 49-68：嘴巴"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "人脸关键点检测任务中，输出为 **人脸关键点的数量x2**，即每个人脸关键点的横坐标与纵坐标。在模型组网时，主要使用2个模块，分别是Inception模块和空间注意力模块。增加空间注意力模块是为了提高模型效果。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "- **Inception模块**\n",
    "\n",
    "GoogLeNet是由Inception模块进行组成的，GoogLeNet采用了模块化的结构，因此修改网络结构时非常简单方便。\n",
    "\n",
    "![](https://ai-studio-static-online.cdn.bcebos.com/ab2001ee42114943af663e5313e18751d43f1262e8b14ae68118a1d54e0ee66a)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import paddle\r\n",
    "import paddle.nn as nn\r\n",
    "\r\n",
    "# GoogLeNet加BN层加速模型收敛\r\n",
    "class Inception(nn.Layer): # 定义Inception块（Inception v1）\r\n",
    "    def __init__(self,c1, c2, c3, c4):\r\n",
    "        super(Inception, self).__init__()\r\n",
    "\r\n",
    "        self.relu = nn.ReLU()\r\n",
    "        self.p1_1 = nn.Conv2D(c1[0], c1[1], 1)\r\n",
    "        self.p2_1 = nn.Conv2D(c1[0], c2[0], 1)\r\n",
    "        self.p2_2 = nn.Conv2D(c2[0], c2[1], 3, padding=1)\r\n",
    "        self.p3_1 = nn.Conv2D(c1[0], c3[0], 1)\r\n",
    "        self.p3_2 = nn.Conv2D(c3[0], c3[1], 5, padding=2)\r\n",
    "        self.p4_1 = nn.MaxPool2D(kernel_size=3, stride=1, padding=1)\r\n",
    "        self.p4_2 = nn.Conv2D(c1[0], c4, 1)\r\n",
    "\r\n",
    "    def forward(self, x):\r\n",
    "        p1 = self.relu(self.p1_1(x))\r\n",
    "        p2 = self.relu(self.p2_2(self.p2_1(x)))\r\n",
    "        p3 = self.relu(self.p3_2(self.p3_1(x)))\r\n",
    "        p4 = self.relu(self.p4_2(self.p4_1(x)))\r\n",
    "\r\n",
    "        return paddle.concat([p1, p2, p3, p4], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "- **空间注意力模块**\n",
    "\n",
    "空间注意力聚焦在“哪里”是最具信息量的部分。计算空间注意力的方法是沿着通道轴应用平均池化和最大池操作，然后将它们连接起来生成一个有效的特征描述符。\n",
    "\n",
    "![](https://ai-studio-static-online.cdn.bcebos.com/056c7bd40bc240478419aa3926b2652145f3a30d6d7d43c7ac356d6949b680f3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import paddle\r\n",
    "import paddle.nn as nn\r\n",
    "\r\n",
    "# 空间注意力机制\r\n",
    "class SAM_Module(nn.Layer):  \r\n",
    "    def __init__(self):  \r\n",
    "        super(SAM_Module, self).__init__()  \r\n",
    "        self.conv_after_concat = nn.Conv2D(in_channels=2, out_channels=1, kernel_size=7, stride=1, padding=3)  \r\n",
    "        self.sigmoid_spatial = nn.Sigmoid()  \r\n",
    "\r\n",
    "    def forward(self, x):  \r\n",
    "        # Spatial Attention Module  \r\n",
    "        module_input = x  \r\n",
    "        avg = paddle.mean(x, axis=1, keepdim=True)  \r\n",
    "        mx = paddle.argmax(x, axis=1, keepdim=True)\r\n",
    "        mx = paddle.cast(mx, 'float32')\r\n",
    "        x = paddle.concat([avg, mx], axis=1)\r\n",
    "        x = self.conv_after_concat(x)  \r\n",
    "        x = self.sigmoid_spatial(x)  \r\n",
    "        x = module_input * x  \r\n",
    "\r\n",
    "        return x  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 2.分类任务\n",
    "\n",
    "CIFAR-10数据集也是分类任务中一个非常经典的数据集，在科研中，常常使用CIFAR数据集评估算法的性能。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### 图像分类\n",
    "\n",
    "基于CIFAR-10数据集实现图像10分类。项目完整代码详见：[从论文到代码深入解析带有门控单元的gMLP算法](https://aistudio.baidu.com/aistudio/projectdetail/2134364)\n",
    "\n",
    "gMLP中，最核心的部分就是**空间选通单元(Spatial Gating Unit,SGU)**，它的结构如下图所示：\n",
    "\n",
    "<img style=\"display: block; margin: 0 auto;\" src=\"https://ai-studio-static-online.cdn.bcebos.com/1cf002e743094b2aab6a4f116d25592f380f0391e1214441bdb6228bcf7e95b1\" width = \"80%\" height = \"80%\" />\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/utils.py:26: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  def convert_to_list(value, n, name, dtype=np.int):\n"
     ]
    }
   ],
   "source": [
    "import paddle\r\n",
    "import paddle.nn as nn\r\n",
    "\r\n",
    "class SpatialGatingUnit(nn.Layer):\r\n",
    "    def __init__(self, dim, dim_seq, act = None):\r\n",
    "        super(SpatialGatingUnit, self).__init__()\r\n",
    "        dim_out = dim // 2\r\n",
    "\r\n",
    "        self.norm = nn.LayerNorm(normalized_shape = dim_out)\r\n",
    "        self.proj = nn.Conv1D(in_channels = dim_seq, out_channels = dim_seq, kernel_size = 1)\r\n",
    "        self.act = act\r\n",
    "\r\n",
    "\r\n",
    "    def forward(self, x):\r\n",
    "        res, gate = paddle.chunk(x, 2, axis=-1)\r\n",
    "        gate = self.norm(gate)\r\n",
    "        gate = self.proj(gate.transpose((0, 2, 1)))\r\n",
    "        if self.act:\r\n",
    "            gate = self.act(gate)\r\n",
    "        y = gate * res.transpose((0, 2, 1))\r\n",
    "        y = y.transpose((0, 2, 1))\r\n",
    "\r\n",
    "        return y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "查看网络结构："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------------------------------------------\n",
      " Layer (type)       Input Shape          Output Shape         Param #    \n",
      "===========================================================================\n",
      "  LayerNorm-1      [[3, 32, 16]]         [3, 32, 16]            32       \n",
      "   Conv1D-1        [[3, 16, 32]]         [3, 16, 32]            272      \n",
      "===========================================================================\n",
      "Total params: 304\n",
      "Trainable params: 304\n",
      "Non-trainable params: 0\n",
      "---------------------------------------------------------------------------\n",
      "Input size (MB): 0.01\n",
      "Forward/backward pass size (MB): 0.02\n",
      "Params size (MB): 0.00\n",
      "Estimated Total Size (MB): 0.04\n",
      "---------------------------------------------------------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'total_params': 304, 'trainable_params': 304}"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgu = SpatialGatingUnit(32, 16)\r\n",
    "paddle.summary(sgu, (3, 32, 32))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "gMLP是由一个个gMLP block构成的，上面我们已经把gMLP block中最重要的空间选通单元(Spatial Gating Unit,SGU)实现了，接下来我们只需要拿来用即可，代码如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import paddle\r\n",
    "import paddle.nn as nn\r\n",
    "\r\n",
    "class gMLPBlock(nn.Layer):\r\n",
    "    def __init__(self, dim, dim_ff, seq_len, act = None):\r\n",
    "        super(gMLPBlock, self).__init__()\r\n",
    "        self.proj_in = nn.Sequential(\r\n",
    "            nn.Linear(dim, dim_ff),\r\n",
    "            nn.GELU()\r\n",
    "        )\r\n",
    "\r\n",
    "        self.sgu = SpatialGatingUnit(dim_ff, seq_len, act)\r\n",
    "        self.proj_out = nn.Linear(dim_ff // 2, dim)\r\n",
    "\r\n",
    "    def forward(self, x):\r\n",
    "        x = self.proj_in(x)\r\n",
    "        x = self.sgu(x)\r\n",
    "        x = self.proj_out(x)\r\n",
    "        return x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "查看网络结构："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------------------------------------\n",
      "   Layer (type)         Input Shape          Output Shape         Param #    \n",
      "===============================================================================\n",
      "     Linear-4          [[3, 32, 32]]         [3, 32, 32]           1,056     \n",
      "      GELU-1           [[3, 32, 32]]         [3, 32, 32]             0       \n",
      "    LayerNorm-2        [[3, 32, 16]]         [3, 32, 16]            32       \n",
      "     Conv1D-2          [[3, 16, 32]]         [3, 16, 32]            272      \n",
      "SpatialGatingUnit-2    [[3, 32, 32]]         [3, 32, 16]             0       \n",
      "     Linear-5          [[3, 32, 16]]         [3, 32, 32]            544      \n",
      "===============================================================================\n",
      "Total params: 1,904\n",
      "Trainable params: 1,904\n",
      "Non-trainable params: 0\n",
      "-------------------------------------------------------------------------------\n",
      "Input size (MB): 0.01\n",
      "Forward/backward pass size (MB): 0.11\n",
      "Params size (MB): 0.01\n",
      "Estimated Total Size (MB): 0.12\n",
      "-------------------------------------------------------------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'total_params': 1904, 'trainable_params': 1904}"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gMLP_block = gMLPBlock(32, 32, 16)\r\n",
    "paddle.summary(gMLP_block, (3, 32, 32))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "将上面的gMLP block堆叠起来，就构成了gMLPs，代码如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import paddle\r\n",
    "import paddle.nn as nn\r\n",
    "\r\n",
    "def pair(val):\r\n",
    "    return (val, val) if not isinstance(val, tuple) else val\r\n",
    "\r\n",
    "class PatchEmbed(nn.Layer):\r\n",
    "    \"\"\" Image to Patch Embedding\r\n",
    "    \"\"\"\r\n",
    "    def __init__(self, img_size=32, patch_size=16, in_chans=3, embed_dim=768):\r\n",
    "        super(PatchEmbed, self).__init__()\r\n",
    "        img_size = (img_size, img_size)\r\n",
    "        patch_size = (patch_size, patch_size)\r\n",
    "        num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])\r\n",
    "        self.img_size = img_size\r\n",
    "        self.patch_size = patch_size\r\n",
    "        self.num_patches = num_patches\r\n",
    "\r\n",
    "        self.project = nn.Conv2D(in_chans, embed_dim, kernel_size=self.patch_size, stride=self.patch_size)\r\n",
    "\r\n",
    "    def forward(self, x):\r\n",
    "        x = self.project(x)\r\n",
    "        x = x.flatten(2)\r\n",
    "        x = paddle.transpose(x, perm=[0, 2, 1])\r\n",
    "        return x\r\n",
    "\r\n",
    "class Residual(nn.Layer):\r\n",
    "    def __init__(self, fn):\r\n",
    "        super(Residual, self).__init__()\r\n",
    "        self.fn = fn\r\n",
    "\r\n",
    "    def forward(self, x):\r\n",
    "        return self.fn(x) + x\r\n",
    "\r\n",
    "class PreNorm(nn.Layer):\r\n",
    "    def __init__(self, dim, fn):\r\n",
    "        super(PreNorm, self).__init__()\r\n",
    "        self.fn = fn\r\n",
    "        self.norm = nn.LayerNorm(dim)\r\n",
    "\r\n",
    "    def forward(self, x, **kwargs):\r\n",
    "        x = self.norm(x)\r\n",
    "        return self.fn(x, **kwargs)\r\n",
    "\r\n",
    "class gMLPVision(nn.Layer):\r\n",
    "    def __init__(\r\n",
    "        self,\r\n",
    "        image_size,\r\n",
    "        patch_size,\r\n",
    "        num_classes,\r\n",
    "        dim,\r\n",
    "        depth,\r\n",
    "        ff_mult = 4,\r\n",
    "        channels = 3,\r\n",
    "    ):\r\n",
    "        super(gMLPVision, self).__init__()\r\n",
    "        self.image_height, self.image_width = pair(image_size)\r\n",
    "        self.patch_height, self.patch_width = pair(patch_size)\r\n",
    "        assert (self.image_height % self.patch_height) == 0 and (self.image_width % self.patch_width) == 0, 'image height and width must be divisible by patch size'\r\n",
    "        num_patches = (self.image_height // self.patch_height) * (self.image_width // self.patch_width)\r\n",
    "\r\n",
    "        dim_ff = dim * ff_mult\r\n",
    "\r\n",
    "        self.to_patch_embed = nn.Sequential(\r\n",
    "            # 'b c (h p1) (w p2) -> b (h w) (c p1 p2)', p1 = patch_height, p2 = patch_width\r\n",
    "            PatchEmbed(img_size=image_size, patch_size=patch_size, in_chans=3, embed_dim=channels * self.patch_height * self.patch_width),\r\n",
    "            nn.Linear(channels * self.patch_height * self.patch_width, dim)\r\n",
    "        )\r\n",
    "\r\n",
    "        self.layers = nn.LayerList([Residual(PreNorm(dim, gMLPBlock(dim = dim, dim_ff = dim_ff, seq_len = num_patches))) for i in range(depth)])\r\n",
    "\r\n",
    "        self.norn = nn.LayerNorm(dim)\r\n",
    "    \r\n",
    "        self.to_logits = nn.Sequential(\r\n",
    "            nn.Linear(dim, num_classes)\r\n",
    "        )\r\n",
    "\r\n",
    "    def forward(self, x):\r\n",
    "        x = self.to_patch_embed(x)\r\n",
    "        layers = self.layers\r\n",
    "        x = nn.Sequential(*layers)(x)\r\n",
    "        x = self.norn(x)\r\n",
    "        x = x.mean(axis=1)\r\n",
    "        return self.to_logits(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "查看gMLPs模型（共30层，维度为128）结构："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "gmlp_vision = gMLPVision(\r\n",
    "    image_size = 32,\r\n",
    "    patch_size = 2,\r\n",
    "    num_classes = 10,\r\n",
    "    dim = 128,\r\n",
    "    depth = 30\r\n",
    ")\r\n",
    "paddle.summary(gmlp_vision, (32, 3, 32, 32))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "```\n",
    "===================================================================\n",
    "Total params: 4,968,486\n",
    "Trainable params: 4,968,486\n",
    "Non-trainable params: 0\n",
    "-------------------------------------------------------------------\n",
    "Input size (MB): 0.38\n",
    "Forward/backward pass size (MB): 4577.50\n",
    "Params size (MB): 18.95\n",
    "Estimated Total Size (MB): 4596.83\n",
    "-------------------------------------------------------------------\n",
    "\n",
    "{'total_params': 4968486, 'trainable_params': 4968486}\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 3.场景任务\n",
    "\n",
    "这里说的场景任务是针对某一个特定的场景开发的深度学习任务，相比于回归和分类任务来说，场景任务的难度更高。这里说的场景任务包括但不限于目标检测、图像分割、文本生成、语音合成、强化学习等。\n",
    "\n",
    "- [PaddleX模型库](https://paddlex.readthedocs.io/zh_CN/develop/appendix/model_zoo.html)\n",
    "- [PaddleGAN模型库](https://gitee.com/paddlepaddle/PaddleGAN/blob/master/docs/zh_CN/apis/apps.md)\n",
    "- [PaddleOCR模型库](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/doc/doc_ch/models_list.md)\n",
    "- [PaddleHub模型库](https://www.paddlepaddle.org.cn/hublist)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### 目标检测\n",
    "\n",
    "基于PaddleX的YOLOv3模型快速实现[昆虫检测](https://aistudio.baidu.com/aistudio/projectdetail/442375)。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "安装PaddleX："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "!pip install paddlex -i https://mirror.baidu.com/pypi/simple"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "PaddleX里的目标检测模型，这里选择骨干网络为DarkNet53的YOLO-V3模型："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'version': '1.3.11',\n",
       " 'Model': 'YOLOv3',\n",
       " '_Attributes': {'model_type': 'detector',\n",
       "  'num_classes': 2,\n",
       "  'labels': None,\n",
       "  'fixed_input_shape': None},\n",
       " '_init_params': {'num_classes': 2,\n",
       "  'backbone': 'DarkNet53',\n",
       "  'anchors': None,\n",
       "  'anchor_masks': None,\n",
       "  'ignore_threshold': 0.7,\n",
       "  'nms_score_threshold': 0.01,\n",
       "  'nms_topk': 1000,\n",
       "  'nms_keep_topk': 100,\n",
       "  'nms_iou_threshold': 0.45,\n",
       "  'label_smooth': False,\n",
       "  'train_random_shapes': [320, 352, 384, 416, 448, 480, 512, 544, 576, 608],\n",
       "  'input_channel': 3},\n",
       " 'completed_epochs': 0}"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import paddlex as pdx\r\n",
    "\r\n",
    "yolo_v3 = pdx.det.YOLOv3(\r\n",
    "    num_classes=2, \r\n",
    "    backbone='DarkNet53'\r\n",
    ")\r\n",
    "\r\n",
    "yolo_v3.get_model_info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### 人像分割\n",
    "\n",
    "基于PaddleX核心分割模型 Deeplabv3+Xcetion65 & HRNet_w18_small_v1 实现[人像分割](https://paddlex.readthedocs.io/zh_CN/develop/examples/human_segmentation.html)，PaddleX提供了人像分割的预训练模型，可直接使用，当然也可以根据自己的数据做微调。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "下载PaddleX源码:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cloning into 'PaddleX'...\n",
      "remote: Enumerating objects: 1890, done.\u001b[K\n",
      "remote: Counting objects: 100% (1890/1890), done.\u001b[K\n",
      "remote: Compressing objects: 100% (682/682), done.\u001b[K\n",
      "remote: Total 20433 (delta 1201), reused 1849 (delta 1180), pack-reused 18543\u001b[K\n",
      "Receiving objects: 100% (20433/20433), 77.88 MiB | 9.81 MiB/s, done.\n",
      "Resolving deltas: 100% (13546/13546), done.\n",
      "Checking connectivity... done.\n"
     ]
    }
   ],
   "source": [
    "!git clone https://gitee.com/PaddlePaddle/PaddleX"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "下载预训练模型的代码位于PaddleX/examples/human_segmentation，进入该目录，并下载预训练模型："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%cd PaddleX/examples/human_segmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "!python pretrain_weights/download_pretrain_weights.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "对人像视频进行分割处理："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "!python video_infer.py --model_dir pretrain_weights/humanseg_mobile_inference --video_path data/test.mp4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "预测结果会保存到output文件夹下的result.avi文件里。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "![](https://ai-studio-static-online.cdn.bcebos.com/6e6d2b5ab2c94b64afb3ee1728bdf7c1f29f454d76d1420286e9d98260d05ae4)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### 文字识别\n",
    "\n",
    "基于chinese_ocr_db_crnn_mobile实现[文字识别](https://www.paddlepaddle.org.cn/hubdetail?name=chinese_ocr_db_crnn_mobile&en_category=TextRecognition)，识别图片当中的汉字，该Module是一个超轻量级中文OCR模型，支持直接预测。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "安装文字识别模型以及必要的资源库："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "!hub install chinese_ocr_db_crnn_mobile==1.1.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "!pip install shapely\r\n",
    "!pip install pyclipper"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "调用预训练模型进行预测："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import paddlehub as hub\r\n",
    "import cv2\r\n",
    "\r\n",
    "ocr = hub.Module(name=\"chinese_ocr_db_crnn_mobile\")\r\n",
    "result = ocr.recognize_text(images=[cv2.imread('/home/aistudio/work/OCR/ocrdemo.png')], \r\n",
    "                            output_dir='/home/aistudio/work/OCR/ocr_result', \r\n",
    "                            visualization=True)\r\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "![](https://ai-studio-static-online.cdn.bcebos.com/46507916316a43d9a0e129c76b1668a03ef8cf9e817a469e854aaf82354cd1ea)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 二、模型训练\n",
    "\n",
    "仅基于原生Python和Numpy看模型训练**基本原理**：[神经网络梯度下降原理详解](https://aistudio.baidu.com/aistudio/projectdetail/757995)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### 1.基于高层API训练模型\n",
    "\n",
    "通过Model.prepare接口来对训练进行提前的配置准备工作，包括设置模型优化器，Loss计算方法，精度计算方法等。\n",
    "\n",
    "```\n",
    "import paddle\n",
    "\n",
    "# 使用paddle.Model完成模型的封装\n",
    "model = paddle.Model(Net)\n",
    "\n",
    "# 为模型训练做准备，设置优化器，损失函数和精度计算方式\n",
    "model.prepare(optimizer=paddle.optimizer.Adam(parameters=model.parameters()),\n",
    "              loss=paddle.nn.CrossEntropyLoss(),\n",
    "              metrics=paddle.metric.Accuracy())\n",
    "\n",
    "# 调用fit()接口来启动训练过程\n",
    "model.fit(train_dataset,\n",
    "          epochs=1,\n",
    "          batch_size=64,\n",
    "          verbose=1)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "#### 加载数据集\n",
    "\n",
    "论文里将gMLP应用到ImageNet上的图像分类任务中，而不使用额外的数据，从而在视觉领域检查gMLP。但是ImageNet数据集较大，为了节省时间，这里使用Cifar10进行验证。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import paddle.vision as vision\r\n",
    "import paddle\r\n",
    "import paddle.vision.transforms as transforms\r\n",
    "from paddle.vision.transforms import Normalize\r\n",
    "\r\n",
    "normalize = transforms.Normalize(\r\n",
    "    [0.4914*255, 0.4822*255, 0.4465*255], [0.2023*255, 0.1994*255, 0.2010*255])\r\n",
    "\r\n",
    "trainTransforms = transforms.Compose([\r\n",
    "                transforms.RandomCrop(32, padding=4),\r\n",
    "                transforms.RandomHorizontalFlip(), \r\n",
    "                transforms.Transpose(),\r\n",
    "                normalize\r\n",
    "            ])\r\n",
    "\r\n",
    "testTransforms = transforms.Compose([\r\n",
    "                transforms.Transpose(),\r\n",
    "                normalize\r\n",
    "            ])\r\n",
    "\r\n",
    "trainset = vision.datasets.Cifar10(mode='train', transform=trainTransforms)\r\n",
    "trainloader = paddle.io.DataLoader(trainset, batch_size=128, num_workers=0, shuffle=True)\r\n",
    "testset = vision.datasets.Cifar10(mode='test', transform=testTransforms)\r\n",
    "testloader = paddle.io.DataLoader(testset, batch_size=128, num_workers=0, shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "#### 模型封装\n",
    "\n",
    "以使用paddle.Model完成模型的封装，将网络结构组合成一个可快速使用高层API进行训练和预测的对象。代码如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model = paddle.Model(gmlp_vision)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "#### 训练参数配置\n",
    "\n",
    "用paddle.Model完成模型的封装后，在训练前，需要对模型进行配置，通过Model.prepare接口来对训练进行提前的配置准备工作，包括设置模型优化器，Loss计算方法，精度计算方法等。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 调用飞桨框架的VisualDL模块，保存信息到目录中。\r\n",
    "callback = paddle.callbacks.VisualDL(log_dir='gMLP_log_dir')\r\n",
    "\r\n",
    "def create_optim(parameters):\r\n",
    "    step_each_epoch = len(trainloader) // 128\r\n",
    "    lr = paddle.optimizer.lr.CosineAnnealingDecay(learning_rate=0.25,\r\n",
    "                                                  T_max=step_each_epoch * 120)\r\n",
    "\r\n",
    "    return paddle.optimizer.Adam(learning_rate=lr,\r\n",
    "                                 parameters=parameters,\r\n",
    "                                 weight_decay=paddle.regularizer.L2Decay(3e-4))\r\n",
    "\r\n",
    "model.prepare(create_optim(model.parameters()),  # 优化器\r\n",
    "              paddle.nn.CrossEntropyLoss(),        # 损失函数\r\n",
    "              paddle.metric.Accuracy(topk=(1, 5))) # 评估指标"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "#### 模型训练\n",
    "\n",
    "做好模型训练的前期准备工作后，调用fit()接口来启动训练过程，需要指定至少3个关键参数：训练数据集，训练轮次和单次训练数据批次大小。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model.fit(trainloader,\r\n",
    "          testloader,\r\n",
    "          epochs=120,\r\n",
    "          eval_freq=2,\r\n",
    "          shuffle=True, \r\n",
    "          save_dir='gMLP_case1_chk_points/',\r\n",
    "          save_freq=20,\r\n",
    "          batch_size=128,\r\n",
    "          callbacks=callback,\r\n",
    "          verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "```\n",
    "Epoch 120/120\n",
    "step 391/391 [==============================] - loss: 1.3216 - acc_top1: 0.6175 - acc_top5: 0.9607 - 311ms/step\n",
    "\n",
    "Eval begin...\n",
    "step 79/79 [==============================] - loss: 0.6662 - acc_top1: 0.6014 - acc_top5: 0.9587 - 92ms/step        \n",
    "Eval samples: 10000\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### 2.使用PaddleX训练模型\n",
    "\n",
    "YOLOv3模型的训练接口示例，函数内置了piecewise学习率衰减策略和momentum优化器。\n",
    "\n",
    "```\n",
    "model.train(\n",
    "    num_epochs=270,\n",
    "    train_dataset=train_dataset,\n",
    "    train_batch_size=8,\n",
    "    eval_dataset=eval_dataset,\n",
    "    learning_rate=0.000125,\n",
    "    lr_decay_epochs=[210, 240],\n",
    "    save_dir='output/yolov3_darknet53',\n",
    "    use_vdl=True)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "#### 配置数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from paddlex.det import transforms\r\n",
    "import paddlex as pdx\r\n",
    "\r\n",
    "# 下载和解压昆虫检测数据集\r\n",
    "insect_dataset = 'https://bj.bcebos.com/paddlex/datasets/insect_det.tar.gz'\r\n",
    "pdx.utils.download_and_decompress(insect_dataset, path='./')\r\n",
    "\r\n",
    "# 定义训练和验证时的transforms\r\n",
    "# API说明 https://paddlex.readthedocs.io/zh_CN/develop/apis/transforms/det_transforms.html\r\n",
    "train_transforms = transforms.Compose([\r\n",
    "    transforms.MixupImage(mixup_epoch=250), transforms.RandomDistort(),\r\n",
    "    transforms.RandomExpand(), transforms.RandomCrop(), transforms.Resize(\r\n",
    "        target_size=608, interp='RANDOM'), transforms.RandomHorizontalFlip(),\r\n",
    "    transforms.Normalize()\r\n",
    "])\r\n",
    "\r\n",
    "eval_transforms = transforms.Compose([\r\n",
    "    transforms.Resize(\r\n",
    "        target_size=608, interp='CUBIC'), transforms.Normalize()\r\n",
    "])\r\n",
    "\r\n",
    "# 定义训练和验证所用的数据集\r\n",
    "# API说明：https://paddlex.readthedocs.io/zh_CN/develop/apis/datasets.html#paddlex-datasets-vocdetection\r\n",
    "train_dataset = pdx.datasets.VOCDetection(\r\n",
    "    data_dir='insect_det',\r\n",
    "    file_list='insect_det/train_list.txt',\r\n",
    "    label_list='insect_det/labels.txt',\r\n",
    "    transforms=train_transforms,\r\n",
    "    shuffle=True)\r\n",
    "eval_dataset = pdx.datasets.VOCDetection(\r\n",
    "    data_dir='insect_det',\r\n",
    "    file_list='insect_det/val_list.txt',\r\n",
    "    label_list='insect_det/labels.txt',\r\n",
    "    transforms=eval_transforms)\r\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "#### 初始化模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 可使用VisualDL查看训练指标，参考https://paddlex.readthedocs.io/zh_CN/develop/train/visualdl.html\r\n",
    "num_classes = len(train_dataset.labels)\r\n",
    "\r\n",
    "# API说明: https://paddlex.readthedocs.io/zh_CN/develop/apis/models/detection.html#paddlex-det-yolov3\r\n",
    "model = pdx.det.YOLOv3(num_classes=num_classes, backbone='DarkNet53')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "#### 模型训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# API说明: https://paddlex.readthedocs.io/zh_CN/develop/apis/models/detection.html#id1\r\n",
    "# 各参数介绍与调整说明：https://paddlex.readthedocs.io/zh_CN/develop/appendix/parameters.html\r\n",
    "model.train(\r\n",
    "    num_epochs=270,\r\n",
    "    train_dataset=train_dataset,\r\n",
    "    train_batch_size=8,\r\n",
    "    eval_dataset=eval_dataset,\r\n",
    "    learning_rate=0.000125,\r\n",
    "    lr_decay_epochs=[210, 240],\r\n",
    "    save_dir='output/yolov3_darknet53',\r\n",
    "    use_vdl=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "```\n",
    "2021-07-15 20:52:54 [INFO]\tDecompressing output/yolov3_darknet53/pretrain/DarkNet53_ImageNet1k_pretrained.tar...\n",
    "2021-07-15 20:52:55 [INFO]\tLoad pretrain weights from output/yolov3_darknet53/pretrain/DarkNet53_ImageNet1k_pretrained.\n",
    "2021-07-15 20:52:55 [INFO]\tThere are 260 varaibles in output/yolov3_darknet53/pretrain/DarkNet53_ImageNet1k_pretrained are loaded.\n",
    "2021-07-15 20:53:03 [INFO]\t[TRAIN] Epoch=1/270, Step=2/21, loss=18702.005859, lr=0.0, time_each_step=3.84s, eta=6:5:32\n",
    "2021-07-15 20:53:04 [INFO]\t[TRAIN] Epoch=1/270, Step=4/21, loss=8156.868164, lr=0.0, time_each_step=2.12s, eta=3:21:43\n",
    "2021-07-15 20:53:04 [INFO]\t[TRAIN] Epoch=1/270, Step=6/21, loss=10086.267578, lr=1e-06, time_each_step=1.53s, eta=2:25:42\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### 3.模型训练通用配置基本原则\n",
    "\n",
    "1. 每个输入数据的维度要保持一致，且一定要和模型输入保持一致。\n",
    "2. 配置学习率衰减策略时，训练的上限轮数一定要计算正确。\n",
    "3. BatchSize不宜过大，太大容易内存溢出，且一般为2次幂。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 三、超参优化"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 1.超参优化的基本概念\n",
    "\n",
    "\n",
    "### 参数\n",
    "\n",
    "参数是机器学习算法的关键，是从训练数据中学习到的，属于模型的一部分。\n",
    "\n",
    "![](https://ai-studio-static-online.cdn.bcebos.com/574d04bab0344f8fbcbbdb9bdb1a6c7393b1d6c550474f7f8056cb0048f086c8)\n",
    "\n",
    "输入一个值（x），乘以权重，结果就是网络的输出值。权重可以随着网络的训练进行更新,从而找到最佳的值,这样网络就能尝试匹配输出值与目标值。\n",
    "\n",
    "这里的权重其实就是一种参数。\n",
    "\n",
    "\n",
    "### 超参数\n",
    "\n",
    "模型的超参数指的是**模型外部**的配置变量，是不能通过训练的进行来估计其取值不同的，且不同的训练任务往往需要不同的超参数。\n",
    "\n",
    "超参数不同，最终得到的模型也是不同的。\n",
    "\n",
    "一般来说，超参数有：**学习率**，**迭代次数**，**网络的层数**，**每层神经元的个数**等等。\n",
    "\n",
    "常见的超参数有以下三类：\n",
    "1. **网络结构**，包括神经元之间的连接关系、层数、每层的神经元数量、激活函数的类型等 .\n",
    "2. **优化参数**，包括优化方法、学习率、小批量的样本数量等 .\n",
    "3. **正则化系数** \n",
    "\n",
    "实践中，当你使⽤神经⽹络解决问题时，**寻找好的超参数其实是一件非常困难的事情**，对于刚刚接触的同学来说，都是\"佛系调优\"，这也是一开始就\"入土\"的原因，没有依据的盲目瞎调肯定是不行的。\n",
    "\n",
    "\n",
    "\n",
    "## 2.手动调整超参数的四大方法\n",
    "\n",
    "我们在使用某一网络时，一般是比较好的论文中出现过的，是证明过的，当然也可以直接套用，然后在这个基础上，调参。\n",
    "\n",
    "可是如果识别的领域不同，比如同样是LeNet网络，在解决手写数字识别时使用的超参数能得到很好的效果，但是在做眼疾识别时，因为数据集的不同，虽然使用同样的超参数，但是效果可能并不理想。\n",
    "\n",
    "在<< Neural Network and Deep Learning >>这本书中，作者给出⼀些⽤于设定超参数的启发式想法。⽬的是帮读者发展出⼀套工作流来确保很好地设置超参数。这里我把书上的内容总结一下，再结合自己的思考，与大家共同探讨调整超参数的方法论。\n",
    "\n",
    "不过呢，**目前不存在⼀种通用的关于正确策略的共同认知**，这也是超参数调节的\"玄学\"之处。\n",
    "\n",
    "\n",
    "### 1）使用提前停止来确定训练的迭代次数\n",
    "\n",
    "这个策略是我的老师最早教我的，个人认为这也是防止过拟合最直接的方式。\n",
    "\n",
    "做法其实很简单，做一个判断，满足条件时退出循环,终止训练：\n",
    "\n",
    "```python\n",
    "for epoch in range(MAX_EPOCH):\n",
    "\t// 训练代码\n",
    "\tprint('{}[TRAIN]epoch {}, iter {}, output loss: {}'.format(timestring, epoch, i, loss.numpy()))\n",
    "\tif ():\n",
    "\t\tbreak\n",
    "\tmodel.train()\n",
    "```\n",
    "\n",
    "\n",
    "那么这个if条件判断就十分重要了,这里有两种方案:\n",
    "\n",
    "1. 分类准确率不再提升时\n",
    "2. loss降到一个想要的范围时\n",
    "\n",
    "\n",
    "\n",
    "#### 分类准确率不再提升时\n",
    "\n",
    "我们需要再明确⼀下什么叫做分类准确率不再提升，这样方可实现提前停止。\n",
    "\n",
    "我们知道，分类准确率在整体趋势下降的时候仍旧会抖动或者震荡。如果我们在准确度刚开始下降的时候就停止，那么肯定会错过更好的选择。⼀种不错的解决方案是如果分类准确率在⼀段时间内不再提升的时候终止。\n",
    "\n",
    "当然这块用loss也是可以的，loss也是一个评判标准。\n",
    "\n",
    "\n",
    "\n",
    "#### loss降到一个想要的范围时\n",
    "\n",
    "这是我经常使用的、更直接的方法。\n",
    "\n",
    "因为网络有时候会在很长时间内于⼀个特定的分类准确率附近形成平缓的局面，然后才会有提升。如果你想获得相当好的性能，第一种方案（分类准确率不再提升时）的规则可能就会太过激进了 —— 停止得太草率。\n",
    "\n",
    "而本方案（loss降到一个想要的范围时）能很好地解决这一问题，但随之而来的问题就是不知不觉地又多了一个超参数，实际应用上，这个用于条件判断的loss值的选择也很困难。\n",
    "\n",
    "### 2）让学习率从高逐渐降低\n",
    "\n",
    "我们⼀直都将学习速率设置为常量。但是，通常采用可变的学习速率更加有效。\n",
    "\n",
    "如果学习率设置的过低，在训练的前期，训练速度会非常慢；而学习率设置地过高，在训练的后期，又会产生震荡，降低模型的精度：\n",
    "\n",
    "![](https://ai-studio-static-online.cdn.bcebos.com/9e259595dbb545cb8e61c4e0bbe68e6c216a86f43b1240fb812eb9be04c3a26e)\n",
    "\n",
    "所以最好是在前期使用一个较大的学习速率让权重变化得更快。越往后，我们可以降低学习速率，这样可以作出更加精良的调整。\n",
    "\n",
    "⼀种自然的观点是使用提前终止的想法。就是保持学习速率为⼀个常量直到验证准确率开始变差，然后按照某个量下降学习速率。我们重复此过程若干次，直到学习速率是初始值的 1/1024（或者1/1000）,然后终止训练。\n",
    "\n",
    "### 3）宽泛策略\n",
    "\n",
    "在使用神经网络来解决新的问题时，⼀个挑战就是获得任何⼀种非寻常的学习，也就是说，达到比随机的情况更好的结果。\n",
    "\n",
    "也许下面的方法能给你带来某些不一样的启发:\n",
    "\n",
    "1. 通过简化网络来加速实验进行更有意义的学习\n",
    "2. 通过更加频繁的监控验证准确率来获得反馈\n",
    "\n",
    "\n",
    "\n",
    "#### 通过简化网络来加速实验进行更有意义的学习\n",
    "\n",
    "假设，我们第⼀次遇到 MNIST 分类问题。刚开始，你很有激情，但是当模型完全失效时，你会就得有些沮丧。\n",
    "\n",
    "此时就可以将问题简化，将十分类问题转化成二分类问题。丢开训练和验证集中的那些除了 0 和 1的那些图像，即我们只识别0和1。然后试着训练⼀个网络来区分 0 和 1。\n",
    "\n",
    "这样一来，不仅仅问题比 10 个分类的情况简化了，同样也会减少 80% 的训练数据，这样就多出了 5 倍的加速。同时也可以保证更快的实验，也能给予你关于如何构建好的网络更快的洞察。\n",
    "\n",
    "#### 通过更加频繁的监控验证准确率来获得反馈\n",
    "\n",
    "这个方法调的其实是输出:\n",
    "\n",
    "```python\n",
    "if i % 200 == 0:\n",
    "\ttimestring = time.strftime(\"%Y-%m-%d %H:%M:%S\",time.localtime(time.time()))\n",
    "\tprint('{}[VALID]epoch {}, iter {}, output loss: {}'.format(timestring, epoch, i, loss.numpy()))\n",
    "```\n",
    "对模型本身并没有任何关系，但是，你能得到更多并且更快地得到反馈，从而快速地实验其他的超参数，或者甚至近同步地进行不同参数的组合的评比。\n",
    "\n",
    "这一点看似不重要，但对于超参数的调整来说,是很重要的一步\n",
    "\n",
    "因为在实际的应用中，很容易会遇到神经网络学习不到任何知识的情况。你可能要花费若干天在调整参数上，仍然没有进展。所以在前期的时候，就应该从实验中尽可能早的获得快速反馈。直觉上看，这看起来简化问题和架构仅仅会降低你的效率。而实际上，这样能够将进度加快，因为你能够更快地找到传达出有意义的信号的网络。⼀旦你获得这些信号，你可以尝试通过微调超参数获得快速的性能提升。\n",
    "\n",
    "这和人生中很多情况⼀样 —— 万事开头难。\n",
    "\n",
    "### 4）小批量数据(mini-batch)大小不必最优\n",
    "\n",
    "假设我们使用大小为 1 的小批量数据。而一般来说，使用只有⼀个样本的小批量数据会带来关于梯度的错误估计。\n",
    "\n",
    "而实际上，误差并不会真的产⽣这个问题。原因在于单⼀的梯度估计不需要绝对精确。我们需要的是确保代价函数保持下降足够精确的估计。\n",
    "\n",
    "这就好像你现在要去北极点，但是只有⼀个不太精确的指南针。如果你不再频繁地检查指南针，指南针会在平均状况下给出正确的⽅向，所以最后你也能抵达北极点。\n",
    "\n",
    "不过使用更大的小批量数据看起来还是显著地能够进行训练加速的。\n",
    "\n",
    "所以，选择最好的小批量数据大小是⼀种折中。小批量数据太小会加长训练时间；而小批量数据太大是不能够足够频繁地更新权重的。你所需要的是选择⼀个折中的值，可以最大化学习的速度。\n",
    "\n",
    "幸运的是，小批量数据大小的选择其实是相对独立的⼀个超参数（网络整体架构外的参数），所以你不需要优化那些参数来寻找好的小批量数据大小。\n",
    "\n",
    "因此，可以选择的方式就是使用某些可以接受的值（不需要是最优的）作为其他参数的选择，然后进行不同小批量数据大小的尝试，就像上面调整学习率那样,画出验证准确率的值随时间（非回合）变化的图，选择得到最快性能提升的小批量数据大小。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 四、效果展示\n",
    "\n",
    "## 1.可视化输入与输出\n",
    "\n",
    "直接可视化输入与输出是最直接的方法。\n",
    "\n",
    "```\n",
    "import numpy as np\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "img = cv2.imread(PATH_TO_IMAGE)\n",
    "img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "\n",
    "plt.imshow(img)\n",
    "plt.show()\n",
    "```\n",
    "\n",
    "![](https://ai-studio-static-online.cdn.bcebos.com/ce8f83a24ec1423789311f8f8bf674741db96dc8990b450188ed7d7d17d596c3)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 2.巧用VisualDL\n",
    "\n",
    "VisualDL文档：[https://ai.baidu.com/ai-doc/AISTUDIO/Dk3e2vxg9#visualdl%E5%B7%A5%E5%85%B7](https://ai.baidu.com/ai-doc/AISTUDIO/Dk3e2vxg9#visualdl%E5%B7%A5%E5%85%B7)\n",
    "\n",
    "<p align=\"center\">\n",
    "  <img src=\"https://user-images.githubusercontent.com/48054808/119312835-709e9980-bca5-11eb-9fce-2bbd2523dedb.gif\" width=\"70%\"/>\n",
    "</p>\n",
    "\n",
    "完整示例代码请参考：[VisualDL2.2全新升级--可视化分析助力模型快速开发](https://aistudio.baidu.com/aistudio/projectdetail/1990920)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "!pip install --upgrade visualdl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### VisualDL可视化流程\n",
    "\n",
    "1. 创建日志文件：\n",
    "\n",
    "*为了快速找到最佳超参，训练9个不同组合的超参实验，创建方式均相同如下：\n",
    "\n",
    "```\n",
    "writer = LogWriter(\"./log/lenet/run1\")\n",
    "```\n",
    "\n",
    "2. 训练前记录每组实验的超参数名称和数值，且记录想要展示的模型指标名称\n",
    "\n",
    "```\n",
    "writer.add_hparams({'learning rate':0.0001, 'batch size':64, 'optimizer':'Adam'}, ['train/loss', 'train/acc'])\n",
    "```\n",
    "\n",
    "**注意：这里记录的想要展示的模型指标为'train/loss'和 'train/acc'，后续切记需要用`add_scalar`接口记录对应数值**\n",
    "\n",
    "3. 训练过程中插入作图语句，记录accuracy和loss的变化趋势，同时将展示于Scalar和HyperParameters两个界面中：\n",
    "```\n",
    "writer.add_scalar(tag=\"train/loss\", step=step, value=cost)\n",
    "\n",
    "writer.add_scalar(tag=\"train/acc\", step=step, value=accuracy)\n",
    "```\n",
    "4. 记录每一批次中的第一张图片：\n",
    "```\n",
    "img = np.reshape(batch[0][0], [28, 28, 1]) * 255\n",
    "\n",
    "writer.add_image(tag=\"train/input\", step=step, img=img)\n",
    "```\n",
    "5. 记录训练过程中每一层网络权重（weight）、偏差（bias）的变化趋势：\n",
    "```\n",
    "writer.add_histogram(tag='train/{}'.format(param), step=step, values=values)\n",
    "```\n",
    "6. 记录分类效果--precision & recall曲线：\n",
    "```\n",
    "writer.add_pr_curve(tag='train/class_{}_pr_curve'.format(i),\n",
    "                     labels=label_i,\n",
    "                     predictions=prediction_i,\n",
    "                     step=step,\n",
    "                     num_thresholds=20)\n",
    "\n",
    "writer.add_roc_curve(tag='train/class_{}_pr_curve'.format(i),\n",
    "                     labels=label_i,\n",
    "                     predictions=prediction_i,\n",
    "                     step=step,\n",
    "                     num_thresholds=20)\n",
    "```\n",
    "7. 保存模型结构：\n",
    "```\n",
    "fluid.io.save_inference_model(dirname='./model', feeded_var_names=['img'],target_vars=[predictions], executor=exe)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 3.权重可视化\n",
    "\n",
    "InterpretDL源码：[https://github.com/PaddlePaddle/InterpretDL](https://github.com/PaddlePaddle/InterpretDL)\n",
    "\n",
    "![](https://ai-studio-static-online.cdn.bcebos.com/4b819555ed114bb48288621b44b3e5f3de04e7381d434693b91d1e88c4862dd8)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "!pip install interpretdl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-07-23 17:26:40,763 - INFO - unique_endpoints {''}\n",
      "2021-07-23 17:26:40,764 - INFO - File /home/aistudio/.cache/paddle/hapi/weights/resnet50.pdparams md5 checking...\n",
      "2021-07-23 17:26:41,126 - INFO - Found /home/aistudio/.cache/paddle/hapi/weights/resnet50.pdparams\n",
      "100%|██████████| 50/50 [00:02<00:00, 24.20it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(224, 224, 3) (3, 224, 224)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import interpretdl as it\r\n",
    "from paddle.vision.models import resnet50\r\n",
    "\r\n",
    "paddle_model = resnet50(pretrained=True)\r\n",
    "sg = it.SmoothGradInterpreter(paddle_model, use_cuda=True)\r\n",
    "gradients = sg.interpret(\"/home/aistudio/8.png\", visual=True, save_path=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ResNet50_result = [n for n, v in paddle_model.named_sublayers()]\r\n",
    "print(ResNet50_result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gradcam = it.GradCAMInterpreter(paddle_model, use_cuda=True)\r\n",
    "\r\n",
    "heatmap = gradcam.interpret(\r\n",
    "        \"/home/aistudio/8.png\",\r\n",
    "        'conv1',\r\n",
    "        visual=True,\r\n",
    "        save_path=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 五、总结与升华\n",
    "\n",
    "- 本文最重要的地方在于神经网络工作的基本原理，只有搞懂了原理，才能更好地进行超参优化，得到的模型效果才会更好。\n",
    "- 对于模型组网，最重要的是学会使用SubClass形式组网，使用套件虽然简单，但是可定制化程度较低，如果是科研需要，建议一定要学会用SubClass形式组网。\n",
    "- 模型训练是本文中最简单的部分，只需要按照文档在操作即可，但是超参数的选择有很多讲究，超参的好坏往往会影响模型的最终结果\n",
    "- 效果展示是一个项目的加分项，如果是科研需要，那么你也需要可视化地展示你的工作成果，这也是十分重要的。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 作者简介\n",
    "\n",
    "> 北京联合大学 机器人学院 自动化专业 2018级 本科生 郑博培\n",
    "\n",
    "> 中国科学院自动化研究所复杂系统管理与控制国家重点实验室实习生\n",
    "\n",
    "> 百度飞桨开发者技术专家 PPDE\n",
    "\n",
    "> 百度飞桨官方帮帮团、答疑团成员\n",
    "\n",
    "> 深圳柴火创客空间 认证会员\n",
    "\n",
    "> 百度大脑 智能对话训练师\n",
    "\n",
    "> 阿里云人工智能、DevOps助理工程师\n",
    "\n",
    "我在AI Studio上获得至尊等级，点亮10个徽章，来互关呀！！！<br>\n",
    "[https://aistudio.baidu.com/aistudio/personalcenter/thirdview/147378](https://aistudio.baidu.com/aistudio/personalcenter/thirdview/147378)\n",
    "\n",
    "![](https://ai-studio-static-online.cdn.bcebos.com/187d359bec3349c5a5e581bea14a4d2fb121952a86c342aea0eca063ed17b9a1)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "PaddlePaddle 2.0.0b0 (Python 3.5)",
   "language": "python",
   "name": "py35-paddle1.2.0"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
